Yihao Pan
Papers
1
Total Citations
5
H-Index
1
About
Yihao Pan is a researcher in industrial automation and intelligent fault diagnosis, with a focus on applying deep learning to robotic systems. His most cited work, "Multi-axis Industrial Robot Fault Diagnosis Model Based on Improved One-Dimensional Convolutional Neural Network" (2021), introduces a novel approach that leverages a refined 1D-CNN architecture to detect and classify faults in multi-axis industrial robots. This contribution addresses a critical challenge in manufacturing—maintaining operational reliability—by enabling real-time, data-driven monitoring without the need for complex feature engineering. Although his citation count is still growing, Pan’s work stands out for its practical integration of advanced neural networks into industrial settings, offering a scalable solution for predictive maintenance. His research bridges the gap between theoretical deep learning and applied robotics, making it valuable for engineers and researchers seeking to enhance system robustness. As the field of intelligent manufacturing expands, Pan’s contributions are poised to gain further recognition, particularly for their potential to reduce downtime and improve safety in automated production lines.
Research Focus
Key Achievements
Top Papers
- 1